Order the pieces of state that must be restored to resume a training run bit-exactly

A training **checkpoint** is much more than the model weights. To resume a run bit-exactly, six different pieces of state all need to come back. First, the model weights themselves. Without them you have nothing to resume. Second, the optimizer state. Adam tracks two moving averages per parameter and a step counter. Lose them and the very next update direction is wrong. Third, the learning-rate scheduler. Warmup and cosine decay depend on knowing how many steps have already passed. A fresh scheduler restarts at step 0 and applies the wrong LR. Fourth, RNG state across Python, NumPy, PyTorch CPU, and CUDA. Dropout masks, data augmentations, and shuffles all draw from those generators. Fresh seeds produce different masks and the run drifts. Fifth, the data iterator position. You want the next batch to be the one the original run would have seen, not batch 0 again. Sixth, training counters: epoch, global step, and the gradient-accumulation micro-step. Logging, eval cadence, and stopping all depend on them.

Bit-exact resume needs six layers of state, and the order matters because each later layer assumes the earlier ones are already in place. **Model weights** come first; they are the largest object and the resume is meaningless without them. **Optimizer state** comes next: Adam stores moments `m` and `v` per parameter plus the step counter, and a missing optimizer makes the first post-resume update a fresh-Adam update with no momentum. The **LR scheduler** is third. Warmup and cosine decay are functions of step index, so if the scheduler restarts at zero you re warm-up and apply a learning rate that has nothing to do with where training actually was. **RNG state** is fourth: Python, NumPy, PyTorch CPU, and CUDA generators each need their own state. Dropout masks, data augmentations, and shuffle orders all draw from them, and a fresh seed silently changes every stochastic decision from step one. The **data iterator** is fifth. The next batch must be the one the original run would have served next, not example zero. Finally, step counters and bookkeeping, epoch index, global step, and the gradient-accumulation micro-step pointer, keep logging, eval cadence, and the LR scheduler consistent. Miss any one layer and the run drifts in a different way: drop optimizer state and momentum dies, drop RNG and dropout patterns shift, drop the iterator and you re-train on already-seen batches.